kotogram 0.0.13

A dual Python/TypeScript library for Japanese text parsing and encoding using kotogram format

Kotogram

A dual Python/TypeScript library for Japanese text parsing and encoding using the kotogram compact format.

Overview

Kotogram provides tools for parsing Japanese text into a compact, linguistically-rich format that encodes part-of-speech, conjugation, and pronunciation information. The library features:

• Abstract parser interface (JapaneseParser) for backend implementations
• Sudachi implementation (SudachiJapaneseParser) using SudachiPy with full dictionary
• Kotogram format - compact representation preserving linguistic features
• Bidirectional conversion between Japanese text and kotogram format
• Dual-language support - Python and TypeScript implementations (TypeScript coming soon)
• Production-quality CI/CD with comprehensive testing and publishing workflows

Project Structure

kotogram/
├── kotogram/                    # Python package
│   ├── __init__.py             # Package exports and version
│   ├── japanese_parser.py      # Abstract JapaneseParser interface
│   └── sudachi_japanese_parser.py # Sudachi implementation
├── src/                         # TypeScript source
│   ├── kotogram.ts             # Kotogram conversion functions
│   └── index.ts                # Package exports
├── tests-py/                    # Python tests
│   └── test_japanese_parser.py # Japanese parser tests
├── tests-ts/                    # TypeScript tests
│   └── kotogram.test.ts
├── .github/workflows/           # CI/CD workflows
│   ├── python_canary.yml       # Python build & test
│   ├── typescript_canary.yml   # TypeScript build & test
│   ├── python_publish.yml      # Publish to PyPI
│   └── typescript_publish.yml  # Publish to npm
├── version.txt                  # Single source of truth for version
├── publish.sh                  # Version bump and publish script
├── pyproject.toml              # Python package configuration
├── package.json                # TypeScript package configuration
└── tsconfig.json               # TypeScript compiler configuration

Quick Start

Japanese Text Parsing

Parse Japanese text into kotogram format with full linguistic information:

Python:

from kotogram import SudachiJapaneseParser, kotogram_to_japanese

# Initialize parser (requires sudachipy and sudachidict_full)
parser = SudachiJapaneseParser(dict_type='full')

# Convert Japanese to kotogram
japanese = "猫を食べる"
kotogram = parser.japanese_to_kotogram(japanese)
# Result: ⌈ˢ猫ᵖn:common_noun⌉⌈ˢをᵖprt:case_particle⌉⌈ˢ食べるᵖv:general:e-ichidan-ba:terminal⌉

# Convert back to Japanese
reconstructed = kotogram_to_japanese(kotogram)
# Result: "猫を食べる"

# With spaces between tokens
spaced = kotogram_to_japanese(kotogram, spaces=True)
# Result: "猫 を 食べる"

# With furigana (IME-style readings in brackets)
with_furigana = kotogram_to_japanese(kotogram, furigana=True)
# Result: "猫[ねこ]を食べる[たべる]"

# Combine options
spaced_furigana = kotogram_to_japanese(kotogram, spaces=True, furigana=True)
# Result: "猫[ねこ] を 食べる[たべる]"

TypeScript:

import { kotogramToJapanese, splitKotogram } from 'kotogram';

// Convert Japanese to kotogram (requires Python parser)
const kotogram = "⌈ˢ猫ᵖn:common_noun⌉⌈ˢをᵖprt:case_particle⌉⌈ˢ食べるᵖv:general:e-ichidan-ba:terminal⌉";

// Convert back to Japanese
const reconstructed = kotogramToJapanese(kotogram);
// Result: "猫を食べる"

// With spaces between tokens
const spaced = kotogramToJapanese(kotogram, { spaces: true });
// Result: "猫 を 食べる"

// With furigana (IME-style readings in brackets)
const withFurigana = kotogramToJapanese(kotogram, { furigana: true });
// Result: "猫[ねこ]を食べる[たべる]"

// Split into tokens
const tokens = splitKotogram(kotogram);
// Result: ["⌈ˢ猫ᵖn:common_noun⌉", "⌈ˢをᵖprt:case_particle⌉", "⌈ˢ食べるᵖv:general:e-ichidan-ba:terminal⌉"]

Kotogram Format

The kotogram format encodes rich linguistic information in a compact representation:

⌈ˢ食べるᵖv:general:e-ichidan-ba:terminalᵇ食べるᵈ食べるʳタベル⌉
  │  │    │ │       │            │         │      │      │
  │  │    │ │       │            │         │      │      └─ pronunciation (ʳ)
  │  │    │ │       │            │         │      └─ lemma (ᵈ)
  │  │    │ │       │            │         └─ base form (ᵇ)
  │  │    │ │       │            └─ conjugation form
  │  │    │ │       └─ conjugation type
  │  │    │ └─ POS detail
  │  │    └─ part-of-speech (ᵖ)
  │  └─ surface form (ˢ)
  └─ token boundary markers (⌈⌉)

Development

Python Development

# Install in development mode
pip install -e .

# Run tests
python -m pytest tests-py/

# Run type checking
mypy kotogram/

# Build package
python -m build

TypeScript Development

# Install dependencies
npm install

# Build
npm run build

# Run tests
npm test

# Type check
npx tsc --noEmit

Testing

Python Tests

Tests are located in tests-py/ and use the unittest framework. They are also compatible with pytest.

Run tests:

python -m unittest discover -s tests-py -p 'test_*.py' -v
# or
python -m pytest tests-py/ -v

TypeScript Tests

Tests are located in tests-ts/ and use Node.js built-in test runner.

Run tests:

npm test

GitHub Workflows

Canary Builds

These workflows run on every push, pull request, and daily at 2 AM UTC:

• .github/workflows/python_canary.yml

  • Testing: Runs on Python 3.8, 3.9, 3.10, 3.11, 3.12 with unittest and pytest
  • Code Coverage: Tracks test coverage and uploads to Codecov
  • Code Quality:
    • Black for code formatting
    • isort for import sorting
    • flake8 for linting (complexity limit: 10)
    • pylint for advanced code quality (minimum score: 8.0)
    • mypy for strict type checking
  • Security:
    • bandit for security vulnerability scanning
    • safety for dependency vulnerability checks
  • Best Practices:
    • Checks for print() statements (should use logging)
    • Detects TODO/FIXME comments
    • Validates README.md and LICENSE files exist
  • Package Validation:
    • Ensures no TypeScript/JavaScript files leak into Python package
    • Verifies package contents and structure

• .github/workflows/typescript_canary.yml

  • Testing: Runs on Node.js 18, 20, 22
  • Type Checking: Strict TypeScript type checking with --noEmit
  • Code Quality:
    • ESLint for linting (if configured)
    • Prettier for code formatting (if configured)
    • Circular dependency detection with madge
  • Performance:
    • Bundle size analysis (warns if >100KB)
  • Security:
    • npm audit for dependency vulnerabilities
  • Best Practices:
    • Checks for console.log() statements
    • Detects TODO/FIXME comments
    • Warns about any types (encourages type safety)
    • Validates package.json metadata (description, keywords, repository, license)
    • Validates README.md and LICENSE files exist
  • Package Validation:
    • Ensures no Python files leak into TypeScript package
    • Verifies dist/ directory contents

Publishing Workflows

These workflows are triggered when a version tag (e.g., v0.0.1) is pushed:

• .github/workflows/python_publish.yml

  • Verifies version consistency across version.txt, kotogram/init.py, and pyproject.toml
  • Builds and publishes to PyPI using trusted publishing
  • Verifies installation from PyPI

• .github/workflows/typescript_publish.yml

  • Verifies version consistency across version.txt and package.json
  • Builds and publishes to npm with provenance
  • Verifies installation from npm

Version Management

Single Source of Truth

The file version.txt contains the current version number (e.g., 0.0.1). This version must be kept in sync across:

• version.txt
• kotogram/init.py (__version__ variable)
• pyproject.toml (version field)
• package.json (version field)

The publish workflows automatically verify this consistency before publishing.

Publishing a New Version

Use the publish.sh script to bump the version and trigger publication:

# Bump patch version (0.0.1 -> 0.0.2)
./publish.sh patch

# Bump minor version (0.0.1 -> 0.1.0)
./publish.sh minor

# Bump major version (0.0.1 -> 1.0.0)
./publish.sh major

The script will:

1. Increment the version number
2. Update all version files
3. Commit the changes
4. Create a git tag (e.g., v0.0.2)
5. Push the commit and tag to GitHub

This triggers both python_publish.yml and typescript_publish.yml workflows.

Badges

The README includes status badges for build status, package versions, and license:

[![Python Canary](https://github.com/jomof/kotogram/actions/workflows/python_canary.yml/badge.svg?branch=main)](https://github.com/jomof/kotogram/actions/workflows/python_canary.yml)
[![TypeScript Canary](https://github.com/jomof/kotogram/actions/workflows/typescript_canary.yml/badge.svg?branch=main)](https://github.com/jomof/kotogram/actions/workflows/typescript_canary.yml)
[![PyPI Version](https://img.shields.io/pypi/v/kotogram.svg)](https://pypi.org/project/kotogram/)
[![npm Version](https://img.shields.io/npm/v/kotogram.svg)](https://www.npmjs.com/package/kotogram)
[![Python Support](https://img.shields.io/pypi/pyversions/kotogram.svg)](https://pypi.org/project/kotogram/)
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)

Note: Update the username in badge URLs if you fork this to your own repository.

Configuration Requirements

PyPI Publishing

To publish to PyPI, configure trusted publishing:

1. Go to PyPI → Your Account → Publishing
2. Add a new publisher with:
   • Repository: jomof/kotogram
   • Workflow: python_publish.yml
   • Environment: pypi

npm Publishing

To publish to npm, you need an npm access token:

1. Create an automation token on npmjs.com
2. Add it as a GitHub secret named NPM_TOKEN
3. Configure the npm environment in your repository settings

API Reference

JapaneseParser (Abstract Base Class)

Abstract interface for Japanese text parsing implementations.

from kotogram import JapaneseParser

class JapaneseParser(ABC):
    @abstractmethod
    def japanese_to_kotogram(self, text: str) -> str:
        """Convert Japanese text to kotogram compact representation."""
        pass

SudachiJapaneseParser

Sudachi-based implementation using SudachiPy with the full dictionary.

from kotogram import SudachiJapaneseParser

# Initialize with full dictionary (recommended)
parser = SudachiJapaneseParser(dict_type='full')

# Or use smaller dictionaries for faster loading
parser_small = SudachiJapaneseParser(dict_type='small')
parser_core = SudachiJapaneseParser(dict_type='core')

# Enable validation mode for debugging unmapped features
parser_strict = SudachiJapaneseParser(dict_type='full', validate=True)
# This will raise descriptive KeyError if any Sudachi features
# are missing from the mapping dictionaries

# Parse Japanese text
kotogram = parser.japanese_to_kotogram("今日は良い天気です")

Parameters:

• dict_type (default: 'full'): Dictionary type to use ('small', 'core', or 'full')
• validate (default: False): When True, raises descriptive KeyError exceptions when encountering unmapped linguistic features. The error message includes:
  • The name of the mapping dictionary (e.g., POS_MAP, CONJUGATED_TYPE_MAP)
  • The unmapped key value

Validation Mode Example:

# With validate=True, unmapped features raise detailed errors
parser = SudachiJapaneseParser(dict_type='full', validate=True)
try:
    kotogram = parser.japanese_to_kotogram("未知の単語")
except KeyError as e:
    # Error message: "Missing mapping in POS_MAP: key='未知品詞' not found."
    print(f"Unmapped feature detected: {e}")

Helper Functions

from kotogram import kotogram_to_japanese, split_kotogram

# Convert kotogram back to Japanese
japanese = kotogram_to_japanese(kotogram_str)
japanese_with_spaces = kotogram_to_japanese(kotogram_str, spaces=True)

# Split kotogram into individual tokens
tokens = split_kotogram(kotogram_str)

Mapping Constants

Global mapping constants are available in japanese_parser module:

from kotogram.japanese_parser import (
    POS_MAP,              # Part-of-speech mappings
    POS1_MAP,             # POS detail level 1
    POS2_MAP,             # POS detail level 2
    CONJUGATED_TYPE_MAP,  # Conjugation type mappings
    CONJUGATED_FORM_MAP,  # Conjugation form mappings
    POS_TO_CHARS,         # POS to character mappings
    CHAR_TO_POS,          # Character to POS mappings
)

License

MIT

Contributing

This is a template project. Feel free to fork and adapt it for your own dual-language libraries!